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• W3097876879 endingPage "365" @default.
• W3097876879 startingPage "365" @default.
• W3097876879 abstract "Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for validating the model and obtaining new information about resistance mechanisms. Different feature extraction methods, such as SNP calling and counting nucleotide k-mers have been proposed for presenting DNA sequences to the model. However, there are trade-offs between interpretability, computational complexity and accuracy for different feature extraction methods. In this study, we have proposed a new feature extraction method, counting amino acid k-mers or oligopeptides, which provides easier model interpretation compared to counting nucleotide k-mers and reaches the same or even better accuracy in comparison with different methods. Additionally, we have trained machine learning algorithms using different feature extraction methods and compared the results in terms of accuracy, model interpretability and computational complexity. We have built a new feature selection pipeline for extraction of important features so that new AMR determinants can be discovered by analyzing these features. This pipeline allows the construction of models that only use a small number of features and can predict resistance accurately." @default.
• W3097876879 created "2020-11-09" @default.
• W3097876879 creator A5018239621 @default.
• W3097876879 creator A5021677656 @default.
• W3097876879 creator A5023224900 @default.
• W3097876879 creator A5038775063 @default.
• W3097876879 date "2020-10-28" @default.
• W3097876879 modified "2023-09-25" @default.
• W3097876879 title "Amino Acid k-mer Feature Extraction for Quantitative Antimicrobial Resistance (AMR) Prediction by Machine Learning and Model Interpretation for Biological Insights" @default.
• W3097876879 cites W1592445079 @default.
• W3097876879 cites W1640894323 @default.
• W3097876879 cites W1806049405 @default.
• W3097876879 cites W1897044430 @default.
• W3097876879 cites W1923407684 @default.
• W3097876879 cites W1964509770 @default.
• W3097876879 cites W1966786855 @default.
• W3097876879 cites W1977184995 @default.
• W3097876879 cites W1979973356 @default.
• W3097876879 cites W1988790447 @default.
• W3097876879 cites W1995049318 @default.
• W3097876879 cites W2019849264 @default.
• W3097876879 cites W2031746735 @default.
• W3097876879 cites W2036815314 @default.
• W3097876879 cites W2038894244 @default.
• W3097876879 cites W2041257508 @default.
• W3097876879 cites W2041282815 @default.
• W3097876879 cites W2056132907 @default.
• W3097876879 cites W2063756105 @default.
• W3097876879 cites W2085410714 @default.
• W3097876879 cites W2086223988 @default.
• W3097876879 cites W2097618796 @default.
• W3097876879 cites W2097859299 @default.
• W3097876879 cites W2098084671 @default.
• W3097876879 cites W2098318339 @default.
• W3097876879 cites W2099837668 @default.
• W3097876879 cites W2104743763 @default.
• W3097876879 cites W2107825969 @default.
• W3097876879 cites W2108234281 @default.
• W3097876879 cites W2109462762 @default.
• W3097876879 cites W2113212016 @default.
• W3097876879 cites W2115744066 @default.
• W3097876879 cites W2116883856 @default.
• W3097876879 cites W2118688136 @default.
• W3097876879 cites W2122082432 @default.
• W3097876879 cites W2122419967 @default.
• W3097876879 cites W2123677996 @default.
• W3097876879 cites W2125266506 @default.
• W3097876879 cites W2132963724 @default.
• W3097876879 cites W2134852597 @default.
• W3097876879 cites W2142678478 @default.
• W3097876879 cites W2144626747 @default.
• W3097876879 cites W2151853434 @default.
• W3097876879 cites W2153140967 @default.
• W3097876879 cites W2156473120 @default.
• W3097876879 cites W2159579053 @default.
• W3097876879 cites W2171535756 @default.
• W3097876879 cites W2194089876 @default.
• W3097876879 cites W2239037555 @default.
• W3097876879 cites W2275004644 @default.
• W3097876879 cites W2285969752 @default.
• W3097876879 cites W2316777574 @default.
• W3097876879 cites W2386571266 @default.
• W3097876879 cites W2430356171 @default.
• W3097876879 cites W2432197333 @default.
• W3097876879 cites W2514721411 @default.
• W3097876879 cites W2559133556 @default.
• W3097876879 cites W2583363792 @default.
• W3097876879 cites W2588242483 @default.
• W3097876879 cites W2592025411 @default.
• W3097876879 cites W2592112838 @default.
• W3097876879 cites W2620970682 @default.
• W3097876879 cites W2739323458 @default.
• W3097876879 cites W2749092259 @default.
• W3097876879 cites W2758908936 @default.
• W3097876879 cites W2764155647 @default.
• W3097876879 cites W2771835478 @default.
• W3097876879 cites W2807938045 @default.
• W3097876879 cites W2897109701 @default.
• W3097876879 cites W2899068429 @default.
• W3097876879 cites W2899841051 @default.
• W3097876879 cites W2903909400 @default.
• W3097876879 cites W2911951081 @default.
• W3097876879 cites W2914371360 @default.
• W3097876879 cites W2922306967 @default.
• W3097876879 cites W2928080709 @default.
• W3097876879 cites W2941071527 @default.
• W3097876879 cites W2943429827 @default.
• W3097876879 cites W2949676527 @default.
• W3097876879 cites W2949848068 @default.
• W3097876879 cites W2950657048 @default.
• W3097876879 cites W2950954328 @default.
• W3097876879 cites W2952668074 @default.
• W3097876879 cites W2953008890 @default.
• W3097876879 cites W2955004976 @default.
• W3097876879 cites W2964389303 @default.
• W3097876879 cites W2968322804 @default.
• W3097876879 cites W2971492877 @default.
• W3097876879 cites W2980278798 @default.

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